Hikaru Tsunekawa
ABSTRACT
Brain-machine interfaces to classify basic responses, "Yes" or "No," are particularly beneficial for individuals with amyotrophic lateral sclerosis (ALS) who have experienced loss of motor function. However, it is challenging to elicit these cognitive thoughts with high performance because of significant individual differences in brain activity. To overcome this challenge, a "sensory association paradigm," which connects sensory stimulation and Yes/No recall, has been introduced. In this study, we explored the potential of employing a genetic algorithm (GA) to reduce analysis time, with the goal of applying this paradigm at the bedside of ALS patients. We utilized galvanic vestibular stimulation (GVS), which causes equilibrium distortion, and coupled it with Yes/No responses through classical conditioning. Electroencephalographic (EEG) signals were recorded when the distortion occurred due to Yes/No recall alone in the absence of GVS, and machine learning was used to classify Yes/No responses. The classification accuracy was compared between the signals of EEG electrodes selected based on the brain activity areas by the distortion of equilibrium (area-selected) and those of electrodes selected by GA (GA-selected), and the GA-selected condition showed significantly higher accuracy than the other conditions. The selected electrodes were relevant to GVS elicitation and cognitive thinking, and the time required for analysis was within 0.1 seconds, indicating that the time required for bedside analysis may be sufficient. Additional features beyond the basic phase locking value (PLVs) used in this study will be incorporated in our future study to enhance the accuracy.
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